There is growing recognition that understanding characteristics of connections between objects can be as important, if not more important, than understanding characteristics of the objects, themselves. Understanding where there is or isn't a connection between two or more objects can aid in recognizing influences that may exist between objects, and understanding the characteristics of a connection between two objects can aid in understanding a likelihood and/or mechanism of an effect by one of the objects on the other.
Therefore, it has become increasingly commonplace to create and store network data sets in which sets of objects and the connections thereamong are represented as a network of nodes that represent the objects and edges extending between pairs of the nodes that represent the connections. The represented objects may be any of a variety of people or inanimate things, and the represented connections thereamong may be any of a variety of physical, chemical, behavioral and/or still other types of connection. As recognition of the usefulness of such network data sets has continued to grow, the quantity, size and complexity of network data sets has also continued to grow, thereby creating various challenges unique to such data sets.